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requirements.txt

@@ -1,4 +1,4 @@
 scipy
 numpy
 matplotlib
-https://gradio-main-build.s3.amazonaws.com/8edc91ff4b45981ad3e96288b404f9143115b178/gradio-3.10.1-py3-none-any.whl
+https://gradio-main-build.s3.amazonaws.com/4f24e146dac42a85de708d147f488fdf418d4a7f/gradio-3.10.1-py3-none-any.whl

run.ipynb

@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: spectogram"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio scipy numpy matplotlib"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import matplotlib.pyplot as plt\n", "import numpy as np\n", "from scipy import signal\n", "\n", "import gradio as gr\n", "\n", "\n", "def spectrogram(audio):\n", "    sr, data = audio\n", "    if len(data.shape) == 2:\n", "        data = np.mean(data, axis=0)\n", "    frequencies, times, spectrogram_data = signal.spectrogram(\n", "        data, sr, window=\"hamming\"\n", "    )\n", "    plt.pcolormesh(times, frequencies, np.log10(spectrogram_data))\n", "    return plt\n", "\n", "\n", "demo = gr.Interface(spectrogram, \"audio\", \"plot\")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}